Supplementary material to "Limitations of WRF land surface models for simulating land use and land cover change in Sub-Saharan Africa and development of an improved model (CLM-AF v. 1.0)"

Abstract. Land use and land cover change (LULCC) impacts local and regional climates through various biogeophysical processes. Accurate representation of land surface parameters in land surface models (LSMs) is essential to accurately predict these LULCC-induced climate signals. In this work, we test the applicability of the default Noah, Noah-MP, and Community Land Model (CLM) LSMs in the Weather Research and Forecasting (WRF) model over Sub-Saharan Africa. We find that the default WRF LSMs do not accurately represent surface albedo, leaf area index, and surface roughness in this region due to various flawed assumptions, including the treatment of the MODIS woody savanna land use and land cover (LULC) category as closed shrubland. Consequently, we developed a WRF CLM version with more accurate African land surface parameters (CLM-AF), designed such that it can be used to evaluate the influence of LULCC. We evaluate meteorological performance for the default LSMs and CLM-AF against observational datasets, gridded products, and satellite estimates. Further, we conduct LULCC experiments with each LSM to determine if differences in land surface parameters impact the LULCC-induced climate responses. Despite clear deficiencies in surface parameters, all LSMs reasonably capture the spatial pattern and magnitude of near-surface temperature and precipitation. However, in the LULCC experiments, inaccuracies in the default LSMs result in illogical localized temperature and precipitation changes. Differences in thermal changes between Noah-MP and CLM-AF indicate that the temperature impacts from LULCC are dependent on the sensitivity of evapotranspiration to LULCC in Sub-Saharan Africa. Errors in land surface parameters indicate that the default WRF LSMs considered are not suitable for LULCC experiments in tropical or Southern Hemisphere regions and that proficient meteorological model performance can mask these issues. We find CLM-AF to be suitable for use in Sub-Saharan Africa LULCC studies, but more work is needed by the WRF community to improve its applicability to other tropical and Southern Hemisphere climates.

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Limitations of WRF land surface models for simulating land use and land cover change in Sub-Saharan Africa and development of an improved model (CLM-AF v. 1.0)

10.5194/gmd-2020-193 ◽

2020 ◽

Author(s):

Timothy Glotfelty ◽

Diana Ramírez-Mejía ◽

Jared Bowden ◽

Adrián Ghilardi ◽

J. Jason West

Keyword(s):

Land Use ◽

Land Surface ◽

Sub Saharan Africa ◽

Land Surface Models ◽

Surface Parameters ◽

Temperature And Precipitation ◽

Surface Models ◽

Climate Signals ◽

Sub Saharan ◽

Land Surface Parameters

Abstract. Land use and land cover change (LULCC) impacts local and regional climates through various biogeophysical processes. Accurate representation of land surface parameters in land surface models (LSMs) is essential to accurately predict these LULCC-induced climate signals. In this work, we test the applicability of the default Noah, Noah-MP, and CLM LSMs in the Weather Research and Forecasting Model (WRF) over Sub-Saharan Africa. We find that the default WRF LSMs do not accurately represent surface albedo, leaf area index, and surface roughness in this region due to various flawed assumptions, including the treatment of the MODIS woody savanna LULC category as closed shrubland. Consequently, we developed a WRF CLM version with more accurate African land surface parameters (CLM-AF), designed such that it can be used to evaluate the influence of LULCC. We evaluate meteorological performance for the default LSMs and CLM-AF against observational datasets, gridded products, and satellite estimates. Further, we conduct LULCC experiments with each LSM to determine if differences in land surface parameters impact the LULCC-induced climate signals. Despite clear deficiencies in surface parameters, all LSMs reasonably capture the spatial pattern and magnitude of near surface temperature and precipitation. However in the LULCC experiments, inaccuracies in the default LSMs result in illogical localized temperature and precipitation climate signals. Differences in thermal climate signals between Noah-MP and CLM-AF indicate that the temperature impacts from LULCC are dependent on the sensitivity of evapotranspiration to LULCC in Sub-Saharan Africa. Errors in land surface parameters indicate that the default WRF LSMs considered are not suitable for LULCC experiments in tropical or Southern Hemisphere regions, and that proficient meteorological model performance can mask these issues. We find CLM-AF to be suitable for use in Sub-Saharan Africa LULCC studies, but more work is needed by the WRF community to improve its applicability to other tropical and Southern Hemisphere climates.

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Biophysics and vegetation cover change: a process-based evaluation framework for confronting land surface models with satellite observations

10.5194/essd-2018-24 ◽

2018 ◽

Cited By ~ 1

Author(s):

Gregory Duveiller ◽

Giovanni Forzieri ◽

Eddy Robertson ◽

Wei Li ◽

Goran Georgievski ◽

...

Keyword(s):

Land Cover ◽

Land Cover Change ◽

Vegetation Cover ◽

Land Surface ◽

Heat Fluxes ◽

Satellite Observations ◽

Evaluation Framework ◽

Land Surface Models ◽

Mitigation And Adaptation ◽

Surface Models

Abstract. Land use and land cover change (LULCC) alter the biophysical properties of the Earth's surface. The associated changes in vegetation cover can perturb the local surface energy balance, which in turn can affect the local climate. The sign and magnitude of this change in climate depends on the specific vegetation transition, its timing and location, as well as on the background climate. Land surface models (LSMs) can be used to simulate such land-climate interactions and study their impact in past and future climates, but their capacity to model biophysical effects accurately across the globe remain unclear due to the complexity of the phenomena. Here we present a framework to evaluate the performance of such models with respect to a dedicated dataset derived from satellite remote sensing observations. Idealized simulations from four LSMs (JULES, ORCHIDEE, JSBACH and CLM) are combined with satellite observations to analyse the changes in radiative and turbulent fluxes caused by 15 specific vegetation cover transitions across geographic, seasonal and climatic gradients. The seasonal variation in net radiation associated with land cover change is the process that models capture best, whereas LSMs perform poorly when simulating spatial and climatic gradients of variation in latent, sensible and ground heat fluxes induced by land cover transitions. We expect that this analysis will help identify model limitations and prioritize efforts in model development as well as to inform where consensus between model and observations is already met, ultimately helping to improve the robustness and consistency of model simulations to better inform land-based mitigation and adaptation policies. The dataset is available at: https://doi.org/10.5281/zenodo.1182145.

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Biophysics and vegetation cover change: a process-based evaluation framework for confronting land surface models with satellite observations

Earth System Science Data ◽

10.5194/essd-10-1265-2018 ◽

2018 ◽

Vol 10 (3) ◽

pp. 1265-1279 ◽

Cited By ~ 18

Author(s):

Gregory Duveiller ◽

Giovanni Forzieri ◽

Eddy Robertson ◽

Wei Li ◽

Goran Georgievski ◽

...

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Land Cover Change ◽

Vegetation Cover ◽

Land Surface ◽

Satellite Observations ◽

Evaluation Framework ◽

Land Surface Models ◽

Mitigation And Adaptation ◽

Surface Models

Abstract. Land use and land cover change (LULCC) alter the biophysical properties of the Earth's surface. The associated changes in vegetation cover can perturb the local surface energy balance, which in turn can affect the local climate. The sign and magnitude of this change in climate depends on the specific vegetation transition, its timing and its location, as well as on the background climate. Land surface models (LSMs) can be used to simulate such land–climate interactions and study their impact in past and future climates, but their capacity to model biophysical effects accurately across the globe remain unclear due to the complexity of the phenomena. Here we present a framework to evaluate the performance of such models with respect to a dedicated dataset derived from satellite remote sensing observations. Idealized simulations from four LSMs (JULES, ORCHIDEE, JSBACH and CLM) are combined with satellite observations to analyse the changes in radiative and turbulent fluxes caused by 15 specific vegetation cover transitions across geographic, seasonal and climatic gradients. The seasonal variation in net radiation associated with land cover change is the process that models capture best, whereas LSMs perform poorly when simulating spatial and climatic gradients of variation in latent, sensible and ground heat fluxes induced by land cover transitions. We expect that this analysis will help identify model limitations and prioritize efforts in model development as well as inform where consensus between model and observations is already met, ultimately helping to improve the robustness and consistency of model simulations to better inform land-based mitigation and adaptation policies. The dataset consisting of both harmonized model simulation and remote sensing estimations is available at https://doi.org/10.5281/zenodo.1182145.

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Forest structure and vegetation dynamics as a driver of global carbon uptake

10.5194/egusphere-egu2020-11676 ◽

2020 ◽

Author(s):

Ben Poulter ◽

Leo Calle ◽

Thomas Pugh ◽

Nathan McDowell ◽

Philippe Ciais ◽

...

Keyword(s):

Land Use ◽

Land Cover Change ◽

Forest Structure ◽

Land Surface ◽

Vegetation Dynamics ◽

Carbon Uptake ◽

Forest Age ◽

Land Surface Models ◽

Surface Models ◽

Global Carbon

<p>The drivers for terrestrial carbon uptake remain unclear despite a clear signal that the land removes the equivalent of up to 25-30% of fossil fuel CO2 emissions each year. Recent work has confirmed sustained carbon uptake by the land that is proportional to anthropogenic emissions, meaning that the land 'sink' has strengthened over the past five decades, and with interannual variability driven by climate. Drivers responsible for sustained uptake include hypotheses related to lengthening growing season length, increasing nitrogen deposition, changes in the ratio of diffuse to direct radiation, and land-use and land cover change. More recently, land-use and land-cover change has been investigated as a driver of land carbon uptake owing to an emergence of global-scale datasets related to canopy disturbance, land use, and forest age. At the same time, land-surface models have increased their realism in terms of moving beyond 'big-leaf' model representation of ecosystems to including vertical structure and horizontal heteorogeneity via size-and-age structured approaches. This presentation will address recent work identified forest structure and vegetation dynamics as a driver for global carbon uptake and provide examples of how remote sensing observations have led to new datasets for initialization land-surface models. Compared to inventory-based approaches, land-surface models initialized with forest age show a lessor role in explaining net terrestrial carbon uptake at global scales, but at regional scales, vegetation structure is a key determinant of carbon exchange. New satellite missions improving forest structure observations are expected to reduce uncertainties and contribute substantially to ongoing land-surface model development.</p>

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A Simple Reclassification Method for Correcting Uncertainty in Land Use/Land Cover Data Sets Used with Land Surface Models

Pure and Applied Geophysics ◽

10.1007/s00024-007-0241-4 ◽

2007 ◽

Vol 164 (8-9) ◽

pp. 1789-1809 ◽

Cited By ~ 7

Author(s):

Joseph G. Alfieri ◽

Dev Niyogi ◽

Margaret A. LeMone ◽

Fei Chen ◽

Souleymane Fall

Keyword(s):

Land Use ◽

Land Cover ◽

Land Surface ◽

Data Sets ◽

Land Use Land Cover ◽

Land Surface Models ◽

Land Cover Data ◽

Surface Models

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Inferring past land-use induced changes in surface albedo from satellite observations: a useful tool to evaluate model simulations

Biogeosciences Discussions ◽

10.5194/bgd-9-12505-2012 ◽

2012 ◽

Vol 9 (9) ◽

pp. 12505-12542

Author(s):

J. P. Boisier ◽

N. de Noblet-Ducoudré ◽

P. Ciais

Keyword(s):

Land Use ◽

Land Cover ◽

Land Surface ◽

Surface Albedo ◽

Vegetation Type ◽

Satellite Observations ◽

Land Surface Models ◽

Historical Changes ◽

Surface Models ◽

Past Land Use

Abstract. Cooling resulting from increases in surface albedo has been identified in several studies as the main biogeophysical effect of past land-use induced land cover changes (LCC) on climate. However, the amplitude of this effect remains quite uncertain due to, among other factors, (a) uncertainties in the magnitude of historical LCC and, (b) differences in the way various models simulate surface albedo and more specifically its dependency on vegetation type and snow cover. We have derived monthly albedo climatologies for croplands and four other land-cover types from MODIS satellite observations. We have then estimated the changes in surface albedo since preindustrial times by combining these climatologies with the land-cover maps of 1870 and 1992 used by modelers in the context of the LUCID intercomparison project. These reconstructions show surface albedo increases larger than 10% (absolute) in winter and 2% in summer between 1870 and 1992 over areas that have experienced intense deforestation in the northern temperate regions. The MODIS-based reconstructions of historical changes in surface albedo were then compared to those simulated by the various models participating to LUCID. The inter-model mean albedo response to LCC shows a similar spatial and seasonal pattern to the one resulting from the reconstructions, that is larger increases in winter than in summer driven by the presence of snow. However, individual models show significant differences with the satellite-based reconstructions, despite the fact that land-cover change maps are the same. Our analyses suggest that the primary reason for those discrepancies is how land-surface models parameterize albedo. Another reason, of secondary importance, results from differences in the simulated snowpack. Our methodology is a useful tool not only to infer observations-based historical changes in land surface variables impacted by LCC, but also to point to major deficiencies within the models; we therefore suggest that it could be more widely developed and used in conjunction with other tools in order to evaluate global land-surface models.

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A Simple Reclassification Method for Correcting Uncertainty in Land Use/Land Cover Data Sets Used with Land Surface Models

Pageoph Topical Volumes - Atmospheric and Oceanic ◽

10.1007/978-3-7643-8493-7_19 ◽

2008 ◽

pp. 1789-1809

Author(s):

Joseph G. Alfieri ◽

Dev Niyogi ◽

Margaret A. Lemone ◽

Fei Chen ◽

Souleymane Fall

Keyword(s):

Land Use ◽

Land Cover ◽

Land Surface ◽

Data Sets ◽

Land Use Land Cover ◽

Land Surface Models ◽

Land Cover Data ◽

Surface Models

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Key Challenges for Land Use Planning and Its Environmental Assessments in the Abuja City-Region, Nigeria

Land ◽

10.3390/land10050443 ◽

2021 ◽

Vol 10 (5) ◽

pp. 443

Author(s):

Evidence Chinedu Enoguanbhor ◽

Florian Gollnow ◽

Blake Byron Walker ◽

Jonas Ostergaard Nielsen ◽

Tobia Lakes

Keyword(s):

Land Use ◽

Land Cover ◽

Environmental Assessment ◽

Land Use Planning ◽

Environmental Sustainability ◽

Sub Saharan Africa ◽

Environmental Assessments ◽

City Regions ◽

Sub Saharan ◽

Land Cover Maps

Land use planning as strategic instruments to guide urban dynamics faces particular challenges in the Global South, including Sub-Saharan Africa, where urgent interventions are required to improve urban and environmental sustainability. This study investigated and identified key challenges of land use planning and its environmental assessments to improve the urban and environmental sustainability of city-regions. In doing so, we combined expert interviews and questionnaires with spatial analyses of urban and regional land use plans, as well as current and future urban land cover maps derived from Geographic Information Systems and remote sensing. By overlaying and contrasting land use plans and land cover maps, we investigated spatial inconsistencies between urban and regional plans and the associated urban land dynamics and used expert surveys to identify the causes of such inconsistencies. We furthermore identified and interrogated key challenges facing land use planning, including its environmental assessment procedures, and explored means for overcoming these barriers to rapid, yet environmentally sound urban growth. The results illuminated multiple inconsistencies (e.g., spatial conflicts) between urban and regional plans, most prominently stemming from conflicts in administrative boundaries and a lack of interdepartmental coordination. Key findings identified a lack of Strategic Environmental Assessment and inadequate implementation of land use plans caused by e.g., insufficient funding, lack of political will, political interference, corruption as challenges facing land use planning strategies for urban and environmental sustainability. The baseline information provided in this study is crucial to improve strategic planning and urban/environmental sustainability of city-regions in Sub-Saharan Africa and across the Global South, where land use planning faces similar challenges to address haphazard urban expansion patterns.

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